Digital electronics have made it possible to record a grey scale or color image of a scene, as a still image, as a series of still images, or as a video. A video is a series of still images that continues for an extended period of time with a specific interval between each image. Analog imaging utilizes photographic film to obtain an image, whereas digital imaging utilizes a focal plane array (FPA) to obtain an image which provides a signal in response to light illumination that is then digitized. The FPA includes an array of light-detecting elements, or pixels, positioned at a focal plane of optics that image a scene. FPAs are limited to collecting information about light emanating from a scene in two dimensions, horizontal (x) and vertical (y), in front of the imaging device, often referred to as the field-of-view (FOV). Most FPAs cannot, by themselves, obtain information about the distance (z) of an object from the FPA without the use of complex, high speed, expensive read-out circuits. A wide variety of imaging techniques have been developed to attempt to extract, from a two-dimensional image, information about the distance of a scene and of three-dimensional objects within that scene.
One approach to obtaining distance information for objects in a scene is based on scanning a laser beam over the scene, and determining the ranges and three-dimensional shapes of objects in a scene based on a phase or temporal delay of the laser beam, following reflection from the object. Some such laser scanners are limited to processing tens of thousands to hundreds of thousands of points per second. Therefore, obtaining a high resolution image of a complex scene may take a large amount of time. Additionally, laser scanning merely provides the value of the distance at each measurement point, resulting in what may be referred to as a “point cloud”; often no color or intensity information is obtained, and additional steps are required to transform the point cloud into a digital representation more suited to human interpretation.
The recent emergence of high quality digital imaging through the use of digital cameras and the like has produced additional avenues for creating 3D representations of objects and scenes. Multi-view reconstruction is a process by which a plurality of two-dimensional images of one or more objects are combined to create a single 3D data representation of the objects. Multi-view reconstruction is typically achieved using a “point cloud” model derived from data that are extracted from two-dimensional images and allocated to a three-dimensional virtual space. The computational challenge to build a point-cloud has been solved many times. However, known solutions have several drawbacks.
For one, most conventional 3D scanning systems that use multi-view reconstruction are very expensive. Conventional multi-view 3D scanning systems are also difficult to set up and often require specialized technical expertise to operate. Additionally, the cameras and other hardware used in these system are often prohibitively expensive for the ordinary consumer. Add to that, these complex cameras take many hours to calibrate, and calibrations often require specialized expertise. Thus, conventional multi-view 3D scanning systems do not permit the ordinary consumer to perform 3D imaging easily and inexpensively.
The 3D scanning market is predicted to reach $4.08 billion in sales by 2018. The emergence of 3D printing technologies have made the ability to scan 3D objects of paramount importance.
Accordingly, there is presently a need for a system and method for 3D imaging which is affordable for the ordinary consumer, and also easy to set up and use.